Spectroscopic finger-printing of raw materials

ABSTRACT

The invention provides a method for the selection of cultivation component batches to be used in the cultivation of a mammalian cell expressing a protein of interest wherein at least two different components are employed in the cultivation.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Application No.PCT/EP2011/069267 having an international filing date of Nov. 3, 2011,the entire contents of which are incorporated herein by reference, andwhich claims benefit under 35 U.S.C. §119 to European Patent ApplicationNo. 10190193.2 filed Nov. 5, 2010.

TECHNICAL FIELD

Herein is reported a method for the evaluation of cultivation materialcomponents with respect to product yield already upon receipt thereofand prior to and without the need to perform a test cultivation.

BACKGROUND OF THE INVENTION

The market for recombinant biopharmaceutical products has been growingconstantly since the early 1980s, when recombinant DNA technology madeit possible to express recombinant proteins in different types ofmicroorganisms like bacteria, yeast or mammalian cells. Since then,these protein products have been used in a wide array of diagnostic andpharmaceutical applications.

As the demand for recombinant proteins rises, the need for highlyeffective and robust production processes is imminent. One of the mostimportant influencing factors for robust and reproducible productionprocesses is the composition of the starting materials, such as culturemedia. Most culture media are complex mixtures of among other thingsinorganic salts, sugars, amino acid, vitamins, organic acids andbuffers. In many cases, complex, not chemically defined raw materialslike protein hydrolyzates of plant or bacterial origin are used topromote cell growth and protein production.

Commonly, raw materials are supplied as powder mixtures and thendissolved in water to form the cultivation medium. In many cases, fornot chemically defined protein hydrolyzates and also for chemicallydefined basal media mixtures, a significant lot-to-lot variability canbe observed, leading to large variations in the yield of recombinantlyproduced therapeutic proteins.

Rapid spectroscopic ‘finger-printing’ techniques like Near-,Mid-Infrared, Raman, or 2D-Fluorescence spectroscopies, are relativelyinexpensive and are well suited to analyze complex mixtures. Thesemethods generate very large amounts of high dimensional data that canonly be handled by chemometric methods like principal component analysis(PCA) or partial least squares (PLS) modeling. The combination ofcomplex spectroscopic methods and chemometrics is commonly used inidentity testing for raw materials or as a tool for the classificationof raw materials.

The use of principal component analysis (PCA) and partial least squares(PLS) for processing and modeling complex data have been reported byNæs, T., et al., (Næs, T., et al., NIR Publications, (2002)). In WO2009/086083 a method for hierarchically organizing data using PLS isreported. An analyzer and method for determining the relative importanceof fractions of biological mixtures is reported in WO 2008/146059. In WO2009/061326 the evaluation of chromatographic materials is reported.

In US 2009/0306932 a rapid classification method for multivariate dataarrays is reported. Analysing spectral data for the selection of acalibration model is reported in EP 2 128 599. In U.S. Pat. No.5,498,875 a signal processing for chemical analysis of samples isreported. A method for classifying scientific materials such as silicatematerials, polymer materials and/or nanomaterials is reported in US2008/0177481. In US 2010/0129857 methods for the isolation andidentification of microorganisms are reported.

SUMMARY OF THE INVENTION

It has been found that the performance of production processes forrecombinant proteins can be predicted based on the combination of NIRand 2D-fluorescence spectra of media components, such as proteinhydrolyzates and/or chemically defined media preparations which are usedas components of a complex cultivation medium.

One aspect as reported herein is a method for the selection ofcultivation media component batches or lots to be used in thecultivation of a mammalian cell expressing a protein of interest whereinat least two different components are employed in the cultivation, usingfor such selection fused spectral data of two different spectroscopictechniques.

In one embodiment the method for the selection of cultivation componentlots to be used in the cultivation of a mammalian cell expressing aprotein of interest wherein at least two different cultivationcomponents are employed in the cultivation comprises the followingsteps:

-   -   a) providing spectra of different lots of a first component        obtained with a first spectroscopic method, spectra of different        lots of a second component obtained with a second spectroscopic        method that is different from the first spectroscopic method,        and the cultivation supernatant yield of the protein of interest        obtained in a cultivation using combinations of these different        lots of the first and the second component,    -   b) identifying a relation of fused spectra after computing        spectra PCA scores with the yield of the cultivation,    -   c) providing a spectrum of a further lot of the first component        obtained with the first spectroscopic method and/or a spectrum        of a further lot of the second component obtained with the        second spectroscopic method, and    -   d) selecting the combination of the provided first component and        the provided second component if the predicted cultivation        supernatant yield based on the relation of fused spectra after        computing spectra PCA scores identified in b) is within +/−10%        of the mean yield provided in a).

In one embodiment the method for the selection of cultivation componentlots to be used in the cultivation of a mammalian cell expressing aprotein of interest wherein at least two different cultivationcomponents are employed in the cultivation comprises the followingsteps:

-   -   a) providing spectra of different lots of a first component        obtained with a first spectroscopic method, spectra of different        lots of a second component obtained with a second spectroscopic        method that is different from the first spectroscopic method,        and the cultivation supernatant yield of the protein of interest        obtained in a cultivation using combinations of these different        lots of the first and the second component,    -   b) processing the spectra, filtering the spectra, smoothing the        spectra, and transforming the spectra to their first derivative,    -   c) identifying patterns in the spectra,    -   d) identifying a relation of the patterns identified in c) with        the yield of the cultivation,    -   e) providing a spectrum of a further lot of the first component        obtained with the first spectroscopic method and/or a spectrum        of a further lot of the second component obtained with the        second spectroscopic method,    -   f) processing the spectra, filtering the spectra, smoothing the        spectra, and transforming the spectra to their first derivative,    -   g) selecting the combination of the provided first component and        the provided second component if the predicted cultivation        supernatant yield based on the relation identified in d) is        within +/−10% of the mean yield provided in a).

In one embodiment the first and second spectroscopic method are selectedfrom NIR spectroscopy, MIR spectroscopy, and 2D-fluorescencespectroscopy.

In one embodiment the processing of the spectra comprises the removingof the water absorption regions and the applying of a multiplicativescatter correction, and/or the filtering comprises a Savitzky-Golayfiltering.

In one embodiment the identifying patterns in the spectra is byprincipal component analysis. In one embodiment the principal componentanalysis is an unfolded principal component analysis. In one embodimentthe unfolding preserves the information of the first mode (sample). Inone embodiment the Savitzky-Golay smoothing is with a window of 19points and a 2^(nd) order polynomial. In one embodiment the data ismean-centered, and the optimal number of principal components is chosenusing the leave-one-out cross validation method.

In one embodiment the processing comprises the exclusion of the regionsof scattering and the interpolation of the removed points. In oneembodiment the final spectra are made up by the emission wavelengthrange of 290 nm to 594 nm and the excitation wavelength range of 230 nmto 575 nm.

In one embodiment the identifying of a relation between spectra fusedand compressed with PCA scores, with cultivation yield at harvest is bypartial least square analysis.

In one embodiment the NIR spectra are collected over the wavenumberregion of 4,784 cm⁻¹ to 8,936 cm⁻¹.

In one embodiment the spectral dimensionality is reduced from 1,039wavenumbers to 3 principal components.

In one embodiment the protein of interest is an antibody, or an antibodyfragment, or an antibody conjugate.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 Distribution of the different tested soy protein hydrolyzate lotson a 2-dimensional space built through PCA based on the original NIRspectra.

FIG. 2 NIR spectra of different soy protein hydrolyzate lots.

FIG. 3 Distribution of the different tested rice protein hydrolyzatelots on a 2-dimensional space built through PCA based on the originalNIR spectra.

FIG. 4 Distribution of the different tested chemically defined basicmedium lots on a 2-dimensional space built through PCA based on theoriginal NIR spectra.

FIG. 5 PCA analysis based on pre-processed spectra of soy proteinhydrolyzates lots.

FIG. 6 PCA analysis based on pre-processed spectra of rice proteinhydrolyzates lots.

FIG. 7 PCA analysis based on pre-processed spectra of chemically definedbasic medium lots.

FIG. 8 Fluorescence EEM landscape of a soy protein hydrolyzate lotsamples.

FIG. 9 Processed fluorescence EEM landscape of a soy protein hydrolyzatelot samples.

FIG. 10 Unfolded fluorescence landscapes into a row of emission spectra.

FIG. 11 Excerpt of unfolded spectra for three different lots of soyprotein hydrolyzate.

FIG. 12 Score plot of PC1×PC2 of a PCA for soy protein hydrolyzates ofthe unfolded EEM landscape.

FIG. 13 Score plot of PC1×PC2 of a PCA for rice protein hydrolyzates ofthe unfolded EEM landscape.

FIG. 14 Score plot of PC1×PC2 of a PCA for chemically defined basicmedium of the unfolded EEM landscape.

FIG. 15 Measured vs. cross-validation predicted plot.

FIG. 16 PLS model correlating NIR spectra of different lots of thechemically defined basic medium and product yield.

FIG. 17 PLS model correlating NIR spectra of different lots of the soyprotein hydrolyzate and the chemically defined basic medium and productyield.

FIG. 18 PLS model correlating fluorescence spectra of different lots ofthe soy protein hydrolyzate and NIR spectra of different lots of thechemically defined basic medium and product yield.

FIG. 19 PLS model correlating fluorescence spectra of different lots ofthe soy protein hydrolyzate and MIR spectra of different lots of thechemically defined basic medium and product yield.

FIG. 20 PLS model correlating NIR spectra of different lots of the soyprotein hydrolyzate and fluorescence spectra of different lots of thechemically defined basic medium and product yield.

FIG. 21 NIR absorption radiations of overtone and combination bands ofcovalent bonds organic molecules.

DETAILED DESCRIPTION OF THE INVENTION

It has been found that the performance of production processes forrecombinant proteins can be predicted based on the combined informationcontained in NIR and 2D-fluorescence spectra of media components, suchas protein hydrolyzates and/or chemically defined media preparationswhich are used as components of a complex cultivation medium.

Herein is reported a method in which spectra from two different(orthogonal) spectroscopy techniques—after processing to make themadditive via variable reduction to principal component analysis (PCA)scores—obtained on two media components used in the fermentation ofrecombinant biopharmaceuticals are combined and models of suchtransformed spectra (inputs) are used to predict the yields at harvest(output) of biopharmaceutical product's cultivations based on mixturesof studied media components with lot-to-lot variability in terms ofdifferent fermentation performance.

By using different (orthogonal) spectroscopies in combination with PCAmethods (to ensure their additivity) and producing process models of theeffect of such cultivation media mixtures on yields at harvest of themain fermentation a predictive capability is established that allowsselecting media lots of each raw material and/or formulating mixturesthat best serve the process goals.

Different lots of individual components forming a complete cultivationmedium vary slightly in their detailed composition but are still withinthe specification given by the manufacturer. In some cases, it ispossible to trace this variability to single ingredients, but mostcommonly the lot-to-lot variability cannot be detected by analyticalmeans. For the evaluation of the influence of different individualcomponent lots on product yield a comparable cultivation of the samemammalian cell line can be repeatedly performed.

Herein are reported 56 cultivations in which nine different lots of asoy protein hydrolyzate, two mixtures of two different soy proteinhydrolyzate lots, five lots of a rice protein hydrolyzate, and six lotsof a chemically defined basic medium powder were employed in thefermentation and feed medium, respectively.

To assess the influence of different soy protein hydrolyzate lots withrespect to product yield comparable cultivations were performed in whichthe same lots of a chemically defined basic medium and a rice proteinhydrolyzate were used in fermentation and feed media. The results can begrouped according to the different soy protein hydrolyzate lotsemployed. The performance of different lots was evaluated based on theproduct yield at similar average inoculation cell density (ICD) values(Table 1).

TABLE 1 chemically soy protein defined rice protein product hydrolyzatebasic medium hydrolyzate at 330 h batch lot No. lot No. lot No. ICD[mg/l] D45KD11 1 1 1 5.7 1319 D45KD12 5.3 1234 D45KD13 5.6 1305 D45KD222 5.3 1023 D45KD23 5.1 1070 D45KD31 3 4.8 1008 D45KD32 4.9 991 D45KD335.3 978

The results obtained for a second set of cultivations are listed inTable 2.

TABLE 2 chemically soy protein defined rice protein product hydrolyzatebasic medium hydrolyzate at 330 h batch lot No. lot No. lot No. ICD[mg/l] D52KD11 1 2 2 6.1 1434 D52KD12 5.0 1411 D52KD13 5.6 1459 D52KD214 5.0 1213 D52KD22 5.3 1243 D52KD23 5.4 1163 D55KD11 5 5.0 1409 D55KD125.4 1426 D55KD13 5.7 1430 D55KD21 2 6.8 1263 D55KD22 6.8 1256 D55KD236.8 1278 D55KD31 6 6.1 1269 D55KD32 6.1 1262 D55KD33 5.8 1265

It can be seen that different lots of the individual components resultin different product yields. In this series of cultivations alsodifferent average ICD values were used. Although having low ICD values,cultivations using lot 1 and lot 5 gave significantly higher productyields than the ones having higher ICD values (lot 3 and lot 6). Thus,different soy protein hydrolyzate lots results in different productionperformance.

Analogously the influence of rice protein hydrolyzate on processperformance can be evaluated (Table 3).

TABLE 3 chemically soy protein defined rice protein product hydrolyzatebasic medium hydrolyzate at 330 h batch lot No. lot No. lot No. ICD[mg/l] D61KD11 3 3 2 5.9 1132 D61KD12 6.0 1085 D61KD13 5.3 1101 D61KD213 6.1 1062 D61KD22 6.1 1056 D61KD23 5.6 1043

Six cultivations were performed and can be grouped according to thedifferent lots of rice protein hydrolyzate used in each of them.Performance of the different rice protein hydrolyzate lots can beevaluated based on the mean product yield. Both groups, i.e. riceprotein hydrolyzate lots, have similar ICD values.

To assess the influence of the chemically defined basic medium on theproduct yield, cultivations can be performed with the same lots of soyprotein hydrolyzate and rice protein hydrolyzate in the fermentationinitial media formulation and feed media. Three series of experimentswere performed (Tables 4, 5 and 6).

The first series comprised six cultivations having soy proteinhydrolyzate lot 3 (as in Table 3) and rice protein hydrolyzate lot 2 (asin Table 2) in the fermentation and feed media. Cultivations weregrouped according to the chemically defined basic medium lot used.Performance of different chemically defined basic medium lots wasevaluated based on the product yield. There is a slight differencebetween the two groups in both the average ICD and average productyield. With lower ICD a lower product formation can be obtained. Thus,the chemically defined basic medium lots have little or no effect onproduct yield.

TABLE 4 chemically soy protein defined rice protein product hydrolyzatebasic medium hydrolyzate at 330 h batch lot No. lot No. lot No. ICD[mg/l] D55KD21 3 2 2 6.8 1263 D55KD22 6.8 1256 D55KD23 6.8 1278 D61KD113 5.9 1132 D61KD12 6.0 1085 D61KD13 5.3 1101

The second series involved six cultivations employing soy proteinhydrolyzate lot 1 (as in Table 2) in the fermentation initial mediaformulation and feed media. Experiments were grouped according to thechemically defined basic medium lot used. No significant ICD differenceswere present. Thus, the differences on product yield are due todifferences in the chemically defined basic medium lots used.

TABLE 5 soy protein chemically defined product hydrolyzate basic mediumlot at 330 h batch lot No. No. ICD [mg/l] D45KD11 1 1 5.7 1319 D45KD125.3 1234 D45KD13 5.6 1205 D52KD11 2 6.1 1434 D52KD12 5.0 1411 D52KD135.6 1459

The third series involved five cultivations having soy proteinhydrolyzate lot 2 in the fermentation initial media formulation and feedmedia. Experiments were grouped according to the chemically definedbasic medium lot used. There is a difference between the two groups inboth the ICD used and the product concentration obtained.

TABLE 6 soy protein chemically defined product hydrolyzate basic mediumlot at 330 h batch lot No. No. ICD [mg/l] D45KD22 2 1 5.3 1023 D45KD235.1 1070 D73KD11 4 4.9 1062 D73KD12 4.3 1112 D73KD13 4.4 1121

From the above it can be seen that there exists a need for raw-materiallot characterization and a need to provide a method in which theobtained data can be used to predict which raw-material lots producehigher yields of product without the need to perform fermentationexperiments.

NIR, MIR, and 2D-fluorescence spectra can be acquired of all lots of thethree different cultivation media components. Thereafter spectraanalysis can be performed with established chemometric methods. A novelway of analyzing the spectral information obtained with these differentsources is reported herein and can be used for predictive modelingpurposes.

NIR spectra of the lots of the raw materials were obtained astriplicates in different time periods. For powder and heterogeneouscoarse samples NIR spectra vary among replicates. Such outlyingreplicates can be eliminated based on their relative location in the PCAscores plot space (Euclidean distance).

NIR spectra of 18 lots of soy protein hydrolyzate, 12 lots of riceprotein hydrolyzate, and 14 lots of chemically defined basic medium wereselected out of all provided measurements. NIR spectra were collectedbetween 4,784 cm⁻¹ and 8,936 cm⁻¹. This spectral region does not containnoisy regions. The observed strong baseline shifts are due to lightscattering associated with different raw-material lots havingdifferences in mean particle size distributions (granularity). Theanalysis of raw spectra without baseline correction allows to focus onvariations mainly caused by physical effects. PCA analysis of rawspectra was performed for each raw material separately.

FIG. 1 shows the distribution of the different tested soy proteinhydrolyzate lots on a 2-dimensional space built through PCA based on theoriginal NIR spectra, capturing 94% of the NIR spectra variance. Thespectral dimensionality was reduced from 1,039 wavenumbers to 3significant principal components. Lots giving high product yield cannotbe discriminated based on this analysis from those giving low productyield. In addition granularity (as seen by different NIR spectrabaselines, FIG. 2) and humidity content (as Karl Fischer measurements)of the samples are also different making a clustering of the lotsaccording to any single property very difficult.

FIG. 3 shows how the tested rice protein hydrolyzate lots distribute ona 2-dimensional space built through PCA based on the original NIRspectra, capturing 92% of the NIR spectra variance. As for the soyprotein hydrolyzate, lots giving high product yield cannot bediscriminated based on this analysis alone from lots giving low productyield. Again, granularity and humidity of the samples change from lot tolot affecting clustering.

FIG. 4 shows the distribution of lots of the chemically defined basicmedium on a 2-dimensional space built through PCA based on the originalNIR spectra, capturing 98% of the NIR spectra variance. As for the soyand rice protein hydrolyzates lots giving high product yield cannot bediscriminated from those giving low product yield based on this analysisalone.

The three analyzed cultivation media components show significantlot-to-lot variability in granularity and humidity content, as can beseen by the NIR spectra obtained. NIR is very sensitive to both thesefactors. Additionally both these factors dominate over smaller but stillsignificant chemical composition differences that might be present.Prior to PCA analysis physical information has to be removed by spectrapre-processing.

Water absorbs very strongly in the NIR region especially in the range offrom 6,900 cm⁻¹ to 7,150 cm⁻¹ and of from 5,160 cm⁻¹ to 5,270 cm⁻¹.These absorption regions are caused by the first overtone of the O—Hstretching band and the combination of the O—H stretching and the O—Hbending bands, respectively. Water absorption regions can be removed.Moreover, the baseline shift can be eliminated by applyingmultiplicative scatter correction (MSC). In order to enhance thevariance between samples, the Savitzky-Golay filtering and smoothingmethod can be applied, and spectra can be transformed to their firstderivative (window of 25 points).

The PCA analysis was performed on previously pre-processed spectra ofsoy protein hydrolyzates (FIG. 5). Almost all very good to goodperforming lots in terms of process yield group at the left-hand side ofthe PCA plot (negative PC1 score values). Conversely, lot 4, whichappears to perform poorly, occupies the space on the right-hand side ofthe plot.

The PCA analysis was performed on previously pre-processed spectra ofrice protein hydrolyzates (FIG. 6). Lots giving very similar yieldscluster together, thus, showing that PCA of pre-processed spectra isadequate and that there is already some lot-to-lot variability that canbe traced to chemical composition of this component raw-material, whichis unrelated to granularity or moisture level.

The PCA analysis of the chemically defined basic mediums' pre-processedspectra (FIG. 7) shows that in general all very good to good performinglots group at the left-hand side of the PCA plot (negative score valuesof PC1). Conversely, lot 3, which appears to perform poorly, occupiesthe space on the right-hand side of the plot. Those results arecomparable with the results obtained for the protein hydrolyzate lots.

Besides NIR spectra, fluorescence excitation-emission spectra (EEM)acquired of different water soluble fermentation raw-materials can beanalyzed. A three-way data array, with excitation wavelengths along thex-axis, emission wavelengths along the y-axis, and intensity along thez-axis can be established. In FIG. 8 a fluorescence EEM landscape of asoy protein hydrolyzate lot samples is shown.

2D-Fluorescence spectra of 19 lots of soy protein hydrolyzate, of 12lots of rice protein hydrolyzate, and of 14 lots of chemically definedbasic medium were obtained. The spectra were obtained using excitationwavelengths from 200 nm to 600 nm, with intervals of 5 nm, and emissionwavelengths also from 200 nm to 600 nm, with intervals of 2 nm, giving atotal of 81 excitation and 201 emission wavelengths.

In order to allow a prediction of cultivation yield based on theanalysis of the raw material a three-way array for each of the rawmaterials can be generated from the individual matrices.

A typical EEM spectrum can be influenced by Rayleigh and Ramanscattering effects, which affect the information content of thefluorescence landscape. To overcome the Rayleigh effect severalstrategies and techniques can be used:

-   -   zeroing the emission wavelengths smaller than the excitation        ones;    -   inserting missing values in the region of scattering;    -   excluding the region of scattering and interpolating the removed        points; or    -   subtracting the background spectra.

It has been found that excluding the region of scattering and theinterpolation of the removed points is most suited in the method asreported herein. The Matlab© algorithm EEMscat can be employedtherefore. This algorithm can be downloaded free from world-wide-website: httt://www.models.kvl.dk/source/EEM_correction/. With thisproceeding the scattering can be removed completely. The spectrum alsoshows pronounced noise along the entire emission axis in the firstexcitation wavelength. This region (200 nm to 225 nm) was excluded fromthe spectra, as well the non-informative emission wavelengths (200 nm to315 nm and 596 nm to 600 nm) and excitation wavelengths (580 nm to 600nm). The resulting spectrum is shown in FIG. 9.

The final soy protein hydrolyzate spectra are made up by the emissionwavelength range of 320 nm to 594 nm and the excitation wavelength rangeof 230 nm to 575 nm, resulting in an array of 19×138×70 elements. Thesame procedure can be followed for the rice protein hydrolyzates and thechemically defined basic medium datasets. Thus, the final rice proteinhydrolyzate spectra are comprised of the emission and excitationwavelength range of 290 nm to 594 nm and 230 nm to 550 nm, respectively,resulting in an array of 12×153×65 elements. The final chemicallydefined basic medium spectra comprises the emission wavelength range of290 nm to 594 nm and the excitation wavelength range of 230 nm to 550nm, resulting in an array of 14×162×60 elements.

In conclusion, a pre-processing of the EEM spectra can be performed foreach raw material data set to enhance signal to noise ratio. Thedifferences between each raw material can thus be clearly seen: the soyprotein hydrolyzate comprises 2 or 3 fluorophores, the rice proteinhydrolyzate comprises 3 fluorophores and the chemically defined basicmedium comprises more than 4 fluorophores.

In order to obtain an overview of raw material lot-to-lot variability, aPCA of the unfolded fluorescence data array can be carried out for eachcomponent raw material. The unfolding procedure can be applied in any ofthe three modes of a three-way array. In order to enhance the lot-to-lotdifferences the unfolding preserving information of the first mode(samples) can be employed. In this way, the fluorescence landscapes canbe unfolded into a row of emission spectra one after the other (FIG.10).

The dimensions of the soy protein hydrolyzate array are 19×138×70(lot×emission wavelength×excitation wavelength). After the unfoldingstrategy, a two-way matrix of size 19×9,960 can be obtained. FIG. 11shows a small part of the resulting spectra for three different lots ofsoy protein hydrolyzate. Noise in the extreme excitation wavelengths canbe seen.

To overcome these deviations, several strategies can be used. It hasbeen found that the Savitzky-Golay smoothing using a window of 19 pointsand 2^(nd) order polynomial to remove noise is best suited, and theMultiplicative Scatter Correction (MSC) is best suited to eliminate thebaseline drift.

Unfolded-PCA was applied to the soy protein hydrolyzate pre-processedmatrix. The data was mean-centered, and the optimal number of principalcomponents was chosen using the leave-one-out cross validation method.FIG. 12 shows the score plot of PC1×PC2 of a PCA covering 96% ofvariance found on the whole unfolded EEM landscape.

After unfolding the resulting rice protein hydrolyzate matrix had thesize 12×9,945. The same pre-processing used for soy protein hydrolyzatewas applied. FIG. 13 shows the score plot of PC1×PC2 of a PCA usingthree principal components covering more than 98% of the variance in theunfolded EEM spectra.

The size of unfolded chemically defined basic medium matrix was14×9,600. The same EEM spectra pre-processing procedure as applied tothe other two media components was used. FIG. 14 shows the score plot ofPC1×PC2 of a PCA using two principal components covering more than 92%of the total variance in the unfolded EEM spectra. As before with NIRspectra for the same media components it was found that lots givinghigher yields are separated from lots giving lower yields in the PCAscore plots of EEM unfolded spectra.

A PLS model can be developed for predicting the product yield at the endof the process based on NIR and/or fluorescence spectra obtained fordifferent lots of each media component and/or their combinations. ThePLS algorithm is given an X block (pre-processed spectra, with orwithout variable selection) and a Y block (product parameter) andcorrelates both by finding the variation in X responsible for changes inY (i.e. maximizing the covariance between both blocks). A basic set canbe defined wherein most of the different lots of raw materials can beincluded. Out of replicate batches having same the lot combinations, theone giving the highest product yield was selected for the calibrationdataset (Table 7).

TABLE 7 soy protein hydrolyzate F/ZF product at 330 h batch lot No.[mg/l] D52KD13 1 1458 D52KD22 4 1232 D55KD13 5 1430 D55KD23 3 1257D55KD31 6 1263 D73KD13 2 1120 D73KD33 7 1044 D79KD22 8 1162

NIR spectra can be pre-processed as described before to remove theinfluence of physical effects originating from different particle sizedistributions. As no replicate spectra were used, the leave-one-outcross-validation method was used as internal validation strategy.

The obtained model was made up of only two LVs but a non-significant R²of 0.139 was obtained. The measured vs. cross-validation predicted plotis presented in FIG. 15.

A PLS model correlating NIR spectra of different lots of the chemicallydefined basic medium and product yield can be built using thecalibration dataset as presented in Table 8.

TABLE 8 chemically defined basic medium F/ZF product at 330 h batch lotNo. [mg/l] D45KD11 1 1314 D52KD13 2 1458 D61KD12 3 1134 D73KD21 4 1147D79KD22 5 1162

The obtained model was made up of only two LVs but again a nonsignificant R² of 0.04 was obtained (FIG. 16).

Considering not only one medium component, but the two most relevantones influencing yield, and also taking into account that differentchemical information is captured by each different spectroscopic methodused, a combination strategy can be used between samespectroscopic/different media components and also between differentspectroscopic/different media components.

The criteria used for selecting calibration and validation batches werebased in getting the widest range possible during calibration (Table 9).

TABLE 9 chemically soy protein defined basic product at hydrolyzate F/ZFmedium 330 h batch lot No. F/ZF lot [mg/l] calibration D45KD11 1 1 1314D45KD31 3 1 999 D52KD13 1 2 1458 D52KD22 4 2 1232 D55KD13 5 2 1430D55KD31 6 2 1263 D61KD12 3 3 1134 D73KD13 2 4 1120 D73KD33 7 4 1044D79KD22 8 5 1162 validation D45KD23 2 1 1061 D55KD23 3 2 1257 D73KD21 84 1147

External validation was done with one third of the data set. Calibrationand validation data (NIR spectra) were pre-processed in the same manneras described before. The obtained prediction model is based on 3 LVs andthe obtained R² reached a significant value of 0.88.

Model accuracy and long term robustness is reflected in a high R² withboth calibration and validation errors being low, with a smalldifference between RMSECV and RMSEP (FIG. 17). In the above case, theprediction error was low (RMSEP=36 mg/l) and did not differ much fromthe RMSECV (126 mg/l).

Thus, it has been found that product yield can be correlated tospectroscopic data from different compounds of a cultivation mediumobtained with a combination of spectroscopic information of same nature(NIR) for the two (most important) process raw-materials or mediacomponents. Each spectrum has 944 wavenumbers and the entire calibrationdataset included in the model is represented by 18,880 variables (10samples×2 raw materials×944 wavenumbers after variable selection). Inorder to reduce the required workload a PCA analysis based on thespectra that were first compressed by converting the containedinformation into a few non-correlated variables was performed. Thetherewith obtained model was simpler and contained only 2 latentvariables (LV) and an R² of 0.81 was obtained.

Different spectroscopic methods capture complementary chemicalinformation. Using two different types of spectroscopic informationimproved the predictive quality of the model. Therefore, fluorescencespectra of soy protein hydrolyzate and NIR spectra of the chemicallydefined basic medium were used (Table 10).

TABLE 10 chemically soy protein defined basic product at hydrolyzateF/ZF medium 330 h batch lot No. F/ZF lot [mg/l] calibration D45KD11 1 11314 D45KD31 3 1 999 D52KD13 1 2 1458 D52KD22 4 2 1232 D55KD13 5 2 1430D55KD31 6 2 1263 D61KD12 3 3 1134 D73KD13 2 4 1120 D73KD33 7 4 1044D79KD22 8 5 1162 validation D45KD23 2 1 1061 D55KD23 3 2 1257 D73KD21 84 1147

Fluorescence spectra and NIR spectrawere compressed to a few principalcomponents after pre-processing as described before. The obtained modelhas only 3 latent variables and an R² of 0.90 was obtained (FIG. 18).This model has better performance when compared to previous models andis more robust since it not only has higher R² value, but also has lowerRMSECV and RMSEP values (ca. 90 mg/l) with a very small differencebetween them.

A further test was made using MIR instead of NIR for the chemicallydefined basic medium. Calibration and validation datasets used were thesame as presented before (see Table 10). Fluorescence and MIR spectrawere pre-processed as described before. The obtained model has 3 latentvariables, an R² of 0.88, and low RMSECV and RMSEP values with nodifference between them (ca. 100 mg/l both), thus showing no significantdifference to the one obtained with the NIR data for the chemicallydefined basic medium (FIG. 19).

The NIR spectra of the soy protein hydrolyzate and fluorescence spectraof the chemically defined basic medium were joined together and theresulting model was evaluated. The calibration and validation datasetsused for building the model were the same as before (see Table 10). Theobtained model has 3 latent variables and a very similar R² value (0.87)(FIG. 20) and RMSECV and RMSEP values (124 mg/l and 60 mg/l,respectively).

With an analytical variance for the reference analytics of product ataround 60 mg/l (5% of 1200 mg/l the average product concentration) mostmodels developed showed a prediction accuracy very close to theexperimental limit.

In conclusion, to achieve a prediction of product yield at 330 h,spectral information of both soy protein hydrolyzate and chemicallydefined basic medium must be used. The use of fluorescence spectroscopydata for the chemically defined basic medium gives slightly lower (buteven though very comparable) prediction errors, than models based on NIRspectroscopic data for the chemically defined basic medium and2D-Fluorescence spectroscopic data for the soy protein hydrolyzate.

The method as reported herein is directed to the combination of spectraof different nature (fluorescence spectra and IR spectra), whichintrinsically have different dimensions (two (2D) and one (1D),respectively), and that requires the operations of first compressingeach spectrum to principal component analysis scores and secondproducing linear combinations of each spectrum scores. The spectra ofdifferent nature are combined by means of a dimensional reduction and alinear combination of those reduced transformed variables (PCA scoresobtained by compressing each spectrum).

Thus, in the method as reported herein spectra of different dimensionsand nature are used to capture in a mixture of two differentfermentation raw materials the components responsible for fermentationperformance of said raw materials and to make predictions offermentation yields for a specific combination of lots.

With the method as reported herein it is possible to predict based onthe spectra of two different raw materials to be used in a fermentationprocess performance 10 to 14 days in advance by determining theconditions at harvest of the fermentation.

The following examples and figures are provided to aid the understandingof the present invention, the true scope of which is set forth in theappended claims. It is understood that modifications can be made in theprocedures set forth without departing from the spirit of the invention.

EXAMPLE Materials and Methods Cell Culture:

The cells were cultivated in shake flasks in a temperature, humidity andcarbon dioxide controlled environment. In order to compare differentlots, media were prepared with these lots and cells were inoculated inshake flasks containing these media. A certain volume of feed medium wasadded daily to the shake flask culture in order to prolong cell growthand achieve higher product concentrations.

Near Infrared Spectroscopy (NIR):

NIR emerges in 1960s into the analytical world, with the work of KarlNorris of the US Department of Agriculture (Siesler et al, 2002). In theelectromagnetic spectrum, the NIR region is located in betweenMid-Infrared and Visible. In a range of wavenumber 4,000-14,000 cm⁻¹(respectively wavelength 700-2,500 nm), the absorption radiation ofovertone and combination bands of covalent bonds such as N—H, O—H andC—H of organic molecules (FIG. 21).

NIR spectra were collected using flat bottom scintillation vials in aBruker MPA FT-NIR system, equipped with a tungsten-halogen source and anInAs detector. Each spectrum was recorded in the wavenumber range of4,999 to 9,003 cm⁻¹, in an average of 32 scans and a spectral resolutionof 8 cm⁻¹.

Mid Infrared Spectroscopy (MIR):

Mid Infrared Spectra were obtained using quartz cuvettes in an Avatar370 FT-IR, Thermo Fischer, Diamant ATR. Each spectrum was recorded inthe wavenumber range of 4,000 to 400 cm⁻¹.

Fluorescence Spectroscopy:

Fluorescence spectroscopy uses irradiation at a certain wavelength toexcite molecules, which will then emit radiation of a differentwavelength. This technique is often used for studying the structure andfunction of macromolecules, especially protein interactions. Tentativeassignment of fluorescence characteristics of chromophores found inproteins and nucleic acids is presented in the following Table.

Absorption Fluorescence Substance I_(max) (nm) _(max) (10⁻³) I_(max)(nm) f_(F) tryptophan 280 5.60 348 0.20 tyrosine 274 1.40 393 0.14phenylalanine 257 0.20 282 0.04 adenine 260 13.40 321 2.60 × 10⁻⁴guanine 275 8.10 329 2.60 × 10⁻⁴ cytosine 267 6.10 313 0.80 × 10⁻⁴uracil 260 9.50 308 0.40 × 10⁻⁴ NADH 340 6.20 470 0.02

2D-fluorescence spectra of cell culture raw materials were obtainedusing excitation wavelengths from 200 nm to 600 nm, with intervals of 5nm, and emission wavelengths also from 200 nm to 600 nm, but withintervals of 2 nm, giving a total of 81 excitation and 201 emissionwavelengths. Emission-excitation fluorescence spectra were measuredusing a Varian Cary Eclipse Spectrometer, over an excitation wavelengthrange from 200 nm to 600 nm with intervals of 5 nm, and emissionwavelength range also from 200 nm to 600 nm, but with intervals of 2 nm,giving a total of 81 excitation and 201 emission wavelengths. Data wascollected using the software Cary Eclipse Bio, Package 1.1.

Spectral Treatment and Chemometrics Analysis:

Spectra pre-processing and chemometrics calculations were performed inMatlab 7.2 (MathWorks, U.S.A.) using PLS toolbox 5.5 (Eigenvector,U.S.A.) and Simca P+ 12.01 (Umetrics, Sweden). Rayleigh and Ramanscatterings were removed using the EEMscat algorithm (Bahram et al,2006).

Multivariate data analysis was performed using PCA (Principal ComponentAnalysis) and PLS (Partial Least Squares). These techniques are based onthe reduction of dimensionality present in the data, allowing theretrieval of relevant information hidden in the massive amount of data.It is made transforming the original measured variables into newvariables called principal components. The PCA analysis was used to findpatterns in the spectra. With the aim to relate these patterns with aparticular parameter, PLS analysis was carried out to build amathematical model able to predict the values of this parameter infuture samples using only the spectral information.

In order to build reliable models, the quality of analyticalmeasurements has fundamental importance. Since noise and unwantedinformation are intrinsic to the measurements, it is necessary topre-treat the obtained spectra.

One of the most common techniques to deal with these problems in the NIRspectra is the Savitzky-Golay smoothing filter (Savitzky, A. and Golay,M. J. E., Anal. Chem., 36 (1964) 1627-1639), and it is commonly used inconjunction with derivatives, which has the advantage of reduce baselineshifts and enhance the significant properties of the spectrum.

For fluorescence spectra, the major problems are related to the Ramanand Rayleigh scattering, which are caused by deviations of the lightthat are not related to the fluorescence properties of the sample. Sincethe wavelength regions affected by scattering are known, the intensitiesmeasured in such particular regions can be removed replacing it byinterpolated points.

The three-way emission-excitation spectra were unfolded with the purposeof have a matrix suitable to the PLS and PCA analysis. A Parafac basedthree way analysis was also done for calibration purposes. (Bahram, M.,et al., J. Chemometrics, 20 (2006) 99-105). The unfolding approachconsists in concatenating two of these three dimensions, keeping theother fixed. In this case, the emission and excitation axis wereconcatenated, maintaining the information of the samples.

1. A method for the selection of cultivation component batches to beused in the cultivation of a mammalian cell expressing a protein ofinterest wherein at least two different components are employed in thecultivation comprising the following steps: a) providing spectra ofdifferent batches of a first component obtained with a firstspectroscopic method selected from NIR spectroscopy and MRI spectroscopyand spectra of a second component obtained with 2D-fluorescencespectroscopy and the cultivation supernatant yield of the protein ofinterest obtained in a cultivation using combinations of these differentbatches of the first and the second component, b) identifying a relationof fused spectra of the two different spectroscopic techniques aftercomputing spectra PCA scores with the yield of the cultivation, c)providing a spectrum of a further batch of the first component obtainedwith the first spectroscopic method and a spectrum of a further batch ofthe second component obtained with the second spectroscopic method, d)selecting the combination of the provided first component and theprovided second component if the predicted cultivation supernatant yieldbased on the relation of fused spectra after computing spectra PCAscores identified in b) is within +/−10% of the mean yield provided ina).
 2. canceled
 3. The method according to claim 1, characterized inthat the identifying is by principal component analysis.
 4. The methodaccording to claim 3, characterized in that the principal componentanalysis is an unfolded principal component analysis.
 5. The methodaccording to claim 4, characterized in that the unfolding preserves theinformation of the first mode (sample).
 6. The method according to claim1, 3, 4 and 5, characterized in that the identifying is of a relationbetween spectra fused and compressed with PCA scores with cultivationyield at harvest by partial least square analysis.
 7. The methodaccording to claim 1, 3, 4 and 5, characterized in that the protein ofinterest is an antibody or an antibody fragment or an antibodyconjugate.